The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Digital medical images are constructed using raw image data obtained from such scanners. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Because of large amounts of image data generated in any given scan, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
In the study and analysis of such medical images, it is often necessary for the radiologist to compare the current study to prior studies of the same subject. The prior studies may be acquired by the same or of different image modality. For example, the radiologist may need to compare and evaluate multiple studies acquired at different times using Magnetic Resonance (MR), Computed Tomography (CT), X-Ray films (XR), Ultrasound (US), Positron Emission Tomography (PET), etc.
It is a highly challenging task to accurately relate information in images that are acquired by different scanners at different times. This is because different modalities have widely different intensity and contrast responses to the different tissue types. In addition, different modalities employ different image formation processes that give rise to modality-specific spatial resolution, field of view and noise characteristics. Even further, some modalities (e.g., MR, CT, PET, etc.) produce a 3D volume of data, while other modalities (e.g., XR, US, etc.) produce 2D images. The task of identifying which point in one image corresponds to a given point in the other image is typically performed entirely manually, which is very time-consuming and error-prone.
Accordingly, there exists a need to provide an improved framework for facilitating comparison of different images.